1. Field of the Invention
This invention relates generally to the field of geophysical prospecting. More particularly, the invention relates to the field of seismic data processing. Specifically, the invention is a method for classifying AVO anomalies in near-offset and far-offset seismic data volumes.
2. Description of the Related Art
AVO (Amplitude Versus Offset) analysis of multiple three-dimensional seismic data volumes requires that the volumes are properly aligned and that AVO anomalies are identified and distinguished from background trends. Recently, neural networks have been employed to assist in the identification and classification of AVO anomalies. Sun et al., xe2x80x9cAVO Inversion by Artificial Neural Networksxe2x80x9d, SEG Calgary 70th Annual Meeting, 2000, discuss a method for training a neural network to predict the far offset response, then using the network to predict the far-offset response everywhere and compare this to the actual far-offset response. They also mention the need to align neural network-predicted and actual far-offsets with cross correlation before making the comparison. There are no details given in their paper regarding the use of sub-sample interpolation, or exploiting or generating time shift and correlation volumes in the process to filter the time shift volume. Furthermore, they do not discuss the training of the neural network on the near and far volumes, derivative AVO volumes, or the use of the cross-correlation volume for AVO classification methodology.
The conventional approach to AVO classification uses the familiar AVO cross plotting methodology to discriminate AVO anomalies from background. The most powerful feature of cross plotting is the ability to interact with the data volumes with dynamic linking to better understand the sensitivity of the spatial and geologic extent of AVO anomalies relative to the region selected in the cross plot. Several related techniques have been used in the oil industry to automate or enhance the calculation of AVO anomalies.
DeGroot, xe2x80x9cA Method for Transforming One or More Seismic Input Cubes to One or More Seismic Output Cubes by Way of Neural Network Mappingxe2x80x9d, EAGE Conference, 1999, states that information can be obtained from multiple volumes including near-offset and far-offset stacks, gradients and intercept, acoustic impedance or 4-D volumes (3-D volumes over time). However, DeGroot does not disclose the methods necessary to perform these operations, nor the methods for dealing with residual alignment between offset volumes.
Balz and Pivot, xe2x80x9cFast Identification of AVO Anomalies Using Classification of Pre-Stack Waveformsxe2x80x9d, discuss a methodology using self-organizing maps or K means clustering to classify AVO response. (SEG 2000 Expanded Abstracts, Society of Exploration Geophysicists International Exposition and Seventieth Annual Meeting, Calgary, Alberta, Aug. 6-11, 2000.) Their method is designed to work with pre-stack seismic data and for a specific interval defined through horizon interpretation. Their method is interval-based, not volume-based. There is no mention of building a 3D consistent time shift volume to time align AVO cubes, or exploiting multiple attributes for AVO classification, such as cross correlation and near and far product with difference. Additionally, they do not mention the use of a probabilistic neural network approach with user defined training.
Thus, there exists a need to generate, in a computationally efficient manner, a rapid method for classifying AVO anomalies in pairs of near-offset and far-offset seismic data volumes. This process must also mimic the process employed by and results obtained manually by the seismic interpreter.
The invention is a method for classifying AVO anomalies in pairs of near-offset and far-offset seismic data volumes. First, a plurality of initial AVO seismic attributes are calculated that are representative of the offset seismic data volumes. A probabilistic neural network is constructed from the calculated initial AVO seismic attributes. AVO anomaly classifications are calculated in a portion of the offset seismic data volumes. The preceding steps are repeated until the calculated AVO anomaly classifications in the portion of the offset seismic data volumes are satisfactory. Finally, AVO anomaly classifications are calculated throughout the offset seismic data volumes using the constructed probabilistic neural network.